Об этом курсе: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.
Автор: Стэнфордский университет
Преподаватели: Daphne Koller, Professor
School of Engineering
| Основные сведения | Course 3 of 3 in the Probabilistic Graphical Models Specialization |
| Уровень | Advanced |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Learning: Overview
This module presents some of the learning tasks for probabilistic graphical models that we will tackle in this course.
1 видео
Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)
This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.
6 видео
- Видео: Regularization: Cost Function
- Видео: Evaluating a Hypothesis
- Видео: Model Selection and Train Validation Test Sets
- Видео: Diagnosing Bias vs Variance
- Видео: Regularization and Bias Variance
Parameter Estimation in Bayesian Networks
This module discusses the simples and most basic of the learning problems in probabilistic graphical models: that of parameter estimation in a Bayesian network. We discuss maximum likelihood estimation, and the issues with it. We then discuss Bayesian estimation and how it can ameliorate these problems.
5 видео
- Видео: Maximum Likelihood Estimation for Bayesian Networks
- Видео: Bayesian Estimation
- Видео: Bayesian Prediction
- Видео: Bayesian Estimation for Bayesian Networks
Оцениваемый: Learning in Parametric Models
Оцениваемый: Bayesian Priors for BNs
НЕДЕЛЯ 2
Learning Undirected Models
In this module, we discuss the parameter estimation problem for Markov networks - undirected graphical models. This task is considerably more complex, both conceptually and computationally, than parameter estimation for Bayesian networks, due to the issues presented by the global partition function.
3 видео
- Видео: Maximum Likelihood for Conditional Random Fields
- Видео: MAP Estimation for MRFs and CRFs
Оцениваемый: Parameter Estimation in MNs
Оцениваемый: CRF Learning for OCR
НЕДЕЛЯ 3
Learning BN Structure
This module discusses the problem of learning the structure of Bayesian networks. We first discuss how this problem can be formulated as an optimization problem over a space of graph structures, and what are good ways to score different structures so as to trade off fit to data and model complexity. We then talk about how the optimization problem can be solved: exactly in a few cases, approximately in most others.
7 видео
- Видео: Likelihood Scores
- Видео: BIC and Asymptotic Consistency
- Видео: Bayesian Scores
- Видео: Learning Tree Structured Networks
- Видео: Learning General Graphs: Heuristic Search
- Видео: Learning General Graphs: Search and Decomposability
Оцениваемый: Structure Scores
Оцениваемый: Tree Learning and Hill Climbing
Оцениваемый: Learning Tree-structured Networks
НЕДЕЛЯ 4
Learning BNs with Incomplete Data
In this module, we discuss the problem of learning models in cases where some of the variables in some of the data cases are not fully observed. We discuss why this situation is considerably more complex than the fully observable case. We then present the Expectation Maximization (EM) algorithm, which is used in a wide variety of problems.
5 видео
- Видео: Expectation Maximization - Intro
- Видео: Analysis of EM Algorithm
- Видео: EM in Practice
- Видео: Latent Variables
Оцениваемый: Learning with Incomplete Data
Оцениваемый: Expectation Maximization
Оцениваемый: Learning with Incomplete Data
НЕДЕЛЯ 5
Learning Summary and Final
This module summarizes some of the issues that arise when learning probabilistic graphical models from data. It also contains the course final.
1 видео
Оцениваемый: Learning: Final Exam
PGM Wrapup
This module contains an overview of PGM methods as a whole, discussing some of the real-world tradeoffs when using this framework in practice. It refers to topics from all three of the PGM courses.
1 видео
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Авторы
Стэнфордский университет
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Стоимость
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Рейтинги и отзывы
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.
Just completed the 3 course specialization. If you're interested (and already have some background) in Machine Learning, this specialization is totally worth it. However, if you have trouble solving any of the quizzes or assignments, do not expect to have any kind of support from the TAs. They simply do not respond to any post in the forum, even if it is related with any bug in the programming assignments source code.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2018 Все права защищены.
Yeah! I managed to finish PGM. I feel ready to explore further. PGM 3 is really helpful. Although many details are not fully discussed, some important intuitions are well illustrated, like EM algorithm and its modification in case of incomplete data. Also, the way the teacher teach set an good example for me to learn to demonstrate complicated things in an easy and vivid way. Thank you so much!